摘要
在交通信号控制中,针对单一控制模型不能很好适应实时交通流变化的问题,提出一种交通信号组合优化控制的方法。根据交通流参数,采用自组织特征映射神经网络(SOM)对交叉口交通状态进行识别,依据路口交通状态,动态选择合适的模糊控制器结构。针对非均衡路网的特点,设计了多级模糊控制器,通过搭接车流使交叉口通行能力最大化。针对多级模糊控制器不具备学习能力的问题,采用混沌遗传算法在线优化模糊控制器参数。以兰州市为例,通过仿真研究,给出了不同控制方法下车辆随时间的平均延误曲线,分析了不同交通流下的平均延误。结果表明所提方法具有较好控制效果。
In traffic signal control,a single control model generally cannot well adapt to the changes of real-time traffic flow.To handle this problem,a combined optimal control method of traffic signals is proposed.According to traffic flow parameters,the self-organizing feature map(SOM) neural network is applied to recognize the traffic state.Then the proper structure of the fuzzy controller is selected dynamically based on the intersection state.A multi-stage fuzzy controller with phase sequence optimization is designed for the characteristics of the non balanced road network.The lap traffic flow is adopted to maximize the capacity of signalized intersection.Parameters of the multi-stage fuzzy controller are optimized by using the chaos genetic algorithm for the problem of functional disability of learning.Taking Lanzhou for example,the average delay curve is given under different control methods by simulation studies and the average delay is analyzed under different traffic flows.The results show that the proposed method has good control effect.
引文
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